Spring 2023 Seminar Series Schedule
NYU CUSP is pleased to host our annual Research Seminar Series, featuring leading voices in the growing field of urban informatics. The seminars will examine real-world challenges facing cities and urban environments around the world, with topics ranging from citizen and social sciences to smart infrastructure. See the Spring 2023 schedule below and stay tuned for updates!
NOTE: Seminars will be held either in-person or virtually on Zoom. In-person seminars will not include a live streaming option. Additionally, in-person seminars are only open to faculty, students, and staff at NYU – they are not open to the public. Virtual seminars will also be recorded and posted to this page.
Check out the Spring 2023 schedule:
The scaling analysis of urban indicators helps to understand important features and consequences of city growth, which impact infrastructural and socioeconomic aspects as well as health outcomes. Several arguments have been posed on the universal character of the drivers that lead to urbanization, which would be manifested by very close scaling behavior in different countries and regions inside them. However, recent studies have uncovered that scaling relationships among urban indicators are influenced by strong regional heterogeneities within regions of the world, specific countries and even regions within a large country like Brazil. We will discuss differences and similarities in scaling results for infrastructural and socio economic indicators, health outcomes using mortality data for a wide set of diseases, and urban landscape and street design metrics. Finally we comment how scaling analysis suggests that distinct political solutions might be necessary to improve life’s quality, even for two regions with similar average values of urban indicators.
Roberto Andrade holds a bachelors degree in Physics from the Universidade Federal da Bahia (1975), a Master (M.Sc.) in Physics from the Universidade de São Paulo (1977) and a Doctorate degree (Dr. rer. nat.) in Physics from the Universität Regensburg (1981). He was a post-doctoral fellow at Free University of Brussels, Belgium (1987) and Potsdam Institute for Climate Research, Germany (1994). He was also a visiting professor at several institutions including Oldenburg, Germany (1988, 1991, 1992), Coruña, Spain (2000), ETH-Zürich, Switzerland (2007-2017). After serving as a statistical physics professor at Federal University of Bahia (retired 2019), Andrade continues to develop academic research activities and supervises doctoral theses in this institution, within the PROPAP/UFBA program. He is a researcher at the Center for Data Integration and Knowledge for Health (CIDACS) of Gonçalo Moniz Institute – FIOCRUZ-BA (from 2017 through present) and served as the General Secretary of the Brazilian Society of Physics (2001-2003).
Experienced in Statistical Physics and Complex Systems, with contributions on the following subjects: Aperiodic magnetic models; Hierarchical lattices; Nonlinear dynamical systems; Complex networks; Self-organized criticality; Analysis of complex systems in biology, economy, climate, and geophysics; Use of Big data to analyze health outcomes and disease spreading.
March 31st, 12 pm to 1 pm ET | Zoom (virtual only)
Active school travel (AST) has been increasingly encouraged by various stakeholders in Ontario, Canada through efforts such as school travel planning. Education strategies like workshops or resources that promote AST are commonly implemented. The framing of AST through such strategies may influence how walking and bicycling to school are perceived by parents. It may also draw attention to AST as an issue affecting children’s health which could motivate behavior change. We used natural language processing, including topic modeling, to examine how AST is framed in publicly available documents from Ontario stakeholders involved in school travel planning. We then compared the findings from these documents to a selection of studies on AST and explored similarities between the two. We found that AST is framed in two ways: i) as a health and environmental issue; and ii) as an accessible and feasible transport option for children and parents. The frames encourage children and parents to adopt AST given its health and environmental benefits by providing resources to support behavior change. The benefits of AST and strategies to support AST that are communicated by stakeholders are consistent with the evidence from academic research. While these frames present AST in a positive light, they may not encourage parents to view current household travel behaviors as unhealthy for their children or their community. Stakeholders promoting AST in Ontario should further problematize the decline of AST and challenge the norm of driving children to school.
Antonio Páez is a Professor in the School of Earth, Environment and Society at McMaster University. He trained as a civil engineer and upon joining McMaster was adopted into geography as his home discipline. Now he specializes in spatial data analysis, discrete choice modeling, transportation systems, accessibility, and urban and health geography. He is listed as author or co-author in more than 130 peer reviewed articles in international academic journals, and also recently released his co-authored book Discrete Choice Analysis with R. He has long standing interests in languages, science, science fiction, fantasy, and art. He lives in Hamilton, Ontario, where the summers are short but hot.
April 7th, 12 pm to 1 pm ET | Zoom (virtual only)
Model-based control has witnessed tremendous progress over the last 100 years. In the era of artificial intelligence and autonomous systems, traditional model-based control-theoretical methods are insufficient to addressing emerging control applications tied to networks of super autonomous systems involving V2X communications and operating in complex dynamically changing environments. Learning-based control is a new topic aimed at developing computationally simple, analytically tractable (reinforcement) learning algorithms. These algorithms yield direct adaptive optimal controllers from data collected online in real time. In this talk, I will first review recent developments in adaptive dynamic programming (ADP) for adaptive optimal control of unknown dynamical systems. Then, I will present robust adaptive dynamic programming (RADP) for continuous-time linear and nonlinear systems with dynamic uncertainties. The effectiveness of RADP as a new framework for data-driven adaptive and optimal nonlinear control design is demonstrated via its applications to autonomous vehicles and biological motor control.
Zhong-Ping Jiang received the M.Sc. degree in statistics from the University of Paris XI, France, in 1989, and the Ph.D. degree in automatic control and mathematics from the Ecole des Mines de Paris, France, in 1993. Currently, he is a Professor of Electrical and Computer Engineering at the Tandon School of Engineering, New York University. His research is in the general fields of dynamical networks and nonlinear control (both model-based and learning-based), with applications to information, mechanical and biological systems.
Prof. Jiang is a recipient of the prestigious Queen Elizabeth II Fellowship Award from the Australian Research Council, CAREER Award from the U.S. National Science Foundation, JSPS Invitation Fellowship from the Japan Society for the Promotion of Science, Distinguished Overseas Chinese Scholar Award from the NSF of China, and several best paper awards. He has served as Deputy Editor-in-Chief, Senior Editor and Associate Editor for numerous journals. Prof. Jiang is a Fellow of the IEEE, IFAC, CAA and AAIA, a foreign member of the Academia Europaea (Academy of Europe), and is among the Clarivate Analytics Highly Cited Researchers. In 2022, he received the Excellence in Research Award from the NYU Tandon School of Engineering.
April 28th, 12:00 pm to 1:00 pm ET | 370 Jay St, Room 1201 (in-person only)
The chemical industry produces more than 70,000 products (1.2 billion tons in total) via thermal processes powered by fossil fuel combustion, accounting for ~5% of the US energy utilization and >30% of the US energy-derived industrial CO2 emissions. Amongst these processes, the production of organic chemical commodities accounts for most of the energy utilization (>1200 TBTU/y), and the electrification of these processes via the implementation of electro-organic reactions could enable the integration of renewable electricity sources with chemical plants and accelerate the decarbonization of the chemical industry. Currently, however, two major challenges prevent the deployment of electro-organic reactions at scale: their low selectivity and their low production rates. To circumvent these barriers, my group combines electrochemical reaction engineering principles and machine-learning methods to accelerate the development of high-performing electro-organic reaction processes.
In this presentation, I will discuss our work on understanding and improving the production of adiponitrile (ADN), a precursor to Nylon 6,6, via the electrohydrodimerization of acrylonitrile (AN). This is the largest and most successful electro-organic reaction deployed in industry and serves as a test case for the development of large-scale organic electrochemical processes. Our investigations on ADN are aimed at uncovering the relationship between the electrochemical environment at and near the electrical double layer (EDL) and reaction performance metrics (i.e., selectivity, efficiency, and productivity). I will discuss general guidelines for electrolyte formulation and provide insights into the role of different electrolyte species (e.g., buffer ions, chelating ions, selectivity-directing ions, and supporting ions) in achieving conversions of AN to ADN with selectivity as high as 83%. I will also present how carefully controlling pulsed electrosynthesis conditions guided by active machine learning can help mitigate mass transport limitations, control the concentration of AN near the EDL and enhance the production rate of ADN by >30%. Our learnings on ADN electrosynthesis helped us to also engineer the electrocatalytic hydrogenation of ADN to hexamethylenediamine (a Nylon 6,6 monomer), achieving the highest reported selectivity to date for this reaction (>95%). To further accelerate the development of high-performing electro-organic processes, my group has recently developed new machine-learning methods for rapid reactor outflow analysis using inexpensive spectroscopic tools and Bayesian optimization methods that leverage physical models to maximize process performance. These new tools are critical components of future autonomous workflows that will help us accelerate the electrification of petrochemical processes with large carbon footprints.
Miguel A. Modestino is the Director of the Sustainable Engineering Initiative and the Donald F. Othmer Associate Professor of Chemical Engineering at New York University (NYU). Miguel obtained his B.S in Chemical Engineering (2007) and M.S. in Chemical Engineering Practice (2008) from the Massachusetts Institute of Technology, and his Ph.D. in Chemical Engineering from the University of California, Berkeley (2013). From 2013-2016, he was a post-doctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland where he served as project manager for the Solar Hydrogen Integrated Nano-electrolysis (SHINE) project. He is a winner of the Global Change Award from the H&M Foundation (2016), the MIT Technology Review Innovators Under 35 Award in Latin America (2017) and Globally (2020), the ACS Petroleum Research Fund Doctoral New Investigator Award (2018), the NSF CAREER Award (2019), the Inaugural NYU Tandon Junior Faculty Research Award (2020), and TED Idea Search Latin America (2021). His research group at NYU focusses on the development of electrochemical technologies for the incorporation of renewable energy into chemical manufacturing. He is also co-founder of Sunthetics Inc., a startup developing machine learning solutions to accelerate the development of sustainable chemical processes.
April 21st, 11:30 am to 12:30 pm ET | 370 Jay St, Room 1201 (in-person only)
Homophily, the tendency for humans to be attracted to and prefer to interact with similar others, can be both problematic and promising. On one hand, a lack of it can hinder human collaboration by making it difficult for diverse collaborators to cohesively bond which is needed to help them leverage their diversity. On the other hand, homophily can promote collaboration by acting as a cohesive glue that quickly bonds strangers together who simply believe they are alike in a meaningful way. Earlier in my career, I sought to mitigate the problems of homophily within human collaborations to help them leverage their differences. Ironically, I now find myself actively exploiting homophily to promote human and robot collaboration. In this talk, I will briefly discuss the findings from my prior research on human collaborations and in greater depth my recent research on human and robot collaboration, its assumptions, and possible implications for society going forward.
Lionel Robert is a Professor in the School of Information at the University of Michigan and an AIS Distinguished Member Cum Laude and an IEEE Senior Member. His research focuses on collaboration through and with technology. He currently serves as director of the Michigan Autonomous Vehicle Research Intergroup Collaboration (MAVRIC). He is an affiliate of the University of Michigan Robotics Institute, the National Center for Institutional Diversity at the University of Michigan, and the Center for Computer-Mediated Communication at Indiana University and a member of the AAAS Community Advisory Board. Dr. Robert’s research has been published in leading information systems and information science journals as well as premier computer and robotics conferences. He has also served on various program committees, including the AAAI Conference on Artificial Intelligence, the ACM Conference on Human Factors in Computing Systems, the ACM Conference on Computer-Supported Cooperative Work, the ACM/IEEE International Conference on Human–Robot Interaction, and the ACM International Conference on Supporting Group Work, and as track co-chair and papers track co-chair for the International Conference on Information Systems and the ACM International Conference on Supporting Group Work, as well as general co-chair for the conference. He has also served or currently serves on the editorial boards of the MIS Quarterly, the Journal of the Association for Information Systems, ACM Transactions on Social Computing, AIS Transactions on Human–Computer Interaction, and Collective Intelligence. His research has been sponsored by the AAA Foundation, Automotive Research Center/U.S. Army, Army Research Laboratory, Toyota Research Institute, MCity, Lieberthal-Rogel Center for Chinese Studies, and the National Science Foundation. Dr. Robert has appeared in print, radio, and/or television for ABC, CNN, CNBC, Michigan Radio, Inc., New York Times, and the Associated Press.
May 5th, 12 pm to 1 pm ET | Zoom (virtual only)